Before we start talking about (PG), let’s refresh our minds with the common characteristics of the methods covered in part two of this book. The central topic in Q-learning is the **value** of the state or action + state pair. Value is defined as the discounted total reward that we can gather from this state or by issuing this particular action from the state. If we know the value, our decision on every step becomes simple and obvious: we just act greedily in terms of value, and that guarantees us good total reward at the end of the episode. So, the values of states (in the case of the Value Iteration method) or state + action (in the case of Q-learning) stand between us and the best reward. To obtain these values, we’ve used the Bellman equation, which expresses the value on the current step via the values on the next step.

In Chapter 1, *What is Reinforcement Learning?*, we defined the entity that tells us what to do in every state as **policy**. As in Q-learning methods, when **values...**